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Measuring the Effects of Confidants on Privacy in Smart Contracts

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Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum (BPM 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 459))

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Abstract

Blockchain Systems provide highly welcome properties such as immutability, observability, availability, and distribution for implementing smart contracts without the need for intermediaries. While the smart contract goals of observability and enforceability can easily be achieved on blockchains, the goal of privity is much harder to tackle. In the context of smart contracts on blockchains, privity aims in limiting the spread of knowledge to the participants with a contractual need-to-know. However, limiting access to data can limit the possible degrees of proactive enforcement of correct decisions and it can negatively impact their availability. Therefore, it can be required to find a proper balance between privity and enforceability or availability requirements. Designers may be forced to include additional participants (confidants) in the decision process only for the sake of enforceability or availability.

In this paper, we introduce measures for assessing the impact of confidants for decisions within smart contracts on privacy. We model smart contracts in form of inter-organizational business processes and provide modeling constructs for privity requirements and the inclusion of confidants.

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Author information

Authors and Affiliations

  1. Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65, 9020, Klagenfurt, Austria

    Julius Köpke & Michael Nečemer

Authors
  1. Julius Köpke
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  2. Michael Nečemer
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Corresponding author

Correspondence to Julius Köpke .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Andrea Marrella

  2. University of Tartu, Tartu, Estonia

    Raimundas Matulevičius

  3. University of Warsaw, Warsaw, Poland

    Renata Gabryelczyk

  4. Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Bernhard Axmann

  5. University of Zagreb, Zagreb, Croatia

    Vesna Bosilj Vukšić

  6. Télécom SudParis, Evry, France

    Walid Gaaloul

  7. University of Ljubljana, Ljubljana, Slovenia

    Mojca Indihar Štemberger

  8. Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  9. Data61, Eveleigh, NSW, Australia

    Qinghua Lu

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Köpke, J., Nečemer, M. (2022). Measuring the Effects of Confidants on Privacy in Smart Contracts. In: Marrella, A., et al. Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-031-16168-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-16168-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16167-4

  • Online ISBN: 978-3-031-16168-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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